This learner provides fitting procedures for support vector machines, using
the routines from e1071 (described in Meyer et al. (2021)
and Chang and Lin (2011)
, the core library to which e1071
is an interface) through a call to the function svm.
A learner object inheriting from Lrnr_base with
methods for training and prediction. For a full list of learner
functionality, see the complete documentation of Lrnr_base.
scale = TRUE: A logical vector indicating the variables to be
scaled. For a detailed description, please consult the documentation
for svm.
type = NULL: SVMs can be used as a classification machine, as a
a regression machine, or for novelty detection. Depending of whether
the outcome is a factor or not, the default setting for this argument
is "C-classification" or "eps-regression", respectively. This may be
overwritten by setting an explicit value. For a full set of options,
please consult the documentation for svm.
kernel = "radial": The kernel used in training and predicting.
You may consider changing some of the optional parameters, depending
on the kernel type. Kernel options include: "linear", "polynomial",
"radial" (the default), "sigmoid". For a detailed description, consult
the documentation for svm.
fitted = TRUE: Logical indicating whether the fitted values
should be computed and included in the model fit object or not.
probability = FALSE: Logical indicating whether the model should
allow for probability predictions.
...: Other parameters passed to svm. See its
documentation for details.
Chang C, Lin C (2011).
“LIBSVM: A library for support vector machines.”
ACM Transactions on Intelligent Systems and Technology, 2(3), 27:1--27:27.
Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F (2021).
e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien.
R package version 1.7-6, https://CRAN.R-project.org/package=e1071.
Other Learners:
Custom_chain,
Lrnr_HarmonicReg,
Lrnr_arima,
Lrnr_bartMachine,
Lrnr_base,
Lrnr_bayesglm,
Lrnr_bilstm,
Lrnr_caret,
Lrnr_cv_selector,
Lrnr_cv,
Lrnr_dbarts,
Lrnr_define_interactions,
Lrnr_density_discretize,
Lrnr_density_hse,
Lrnr_density_semiparametric,
Lrnr_earth,
Lrnr_expSmooth,
Lrnr_gam,
Lrnr_ga,
Lrnr_gbm,
Lrnr_glm_fast,
Lrnr_glmnet,
Lrnr_glm,
Lrnr_grf,
Lrnr_gru_keras,
Lrnr_gts,
Lrnr_h2o_grid,
Lrnr_hal9001,
Lrnr_haldensify,
Lrnr_hts,
Lrnr_independent_binomial,
Lrnr_lightgbm,
Lrnr_lstm_keras,
Lrnr_mean,
Lrnr_multiple_ts,
Lrnr_multivariate,
Lrnr_nnet,
Lrnr_nnls,
Lrnr_optim,
Lrnr_pca,
Lrnr_pkg_SuperLearner,
Lrnr_polspline,
Lrnr_pooled_hazards,
Lrnr_randomForest,
Lrnr_ranger,
Lrnr_revere_task,
Lrnr_rpart,
Lrnr_rugarch,
Lrnr_screener_augment,
Lrnr_screener_coefs,
Lrnr_screener_correlation,
Lrnr_screener_importance,
Lrnr_sl,
Lrnr_solnp_density,
Lrnr_solnp,
Lrnr_stratified,
Lrnr_subset_covariates,
Lrnr_tsDyn,
Lrnr_ts_weights,
Lrnr_xgboost,
Pipeline,
Stack,
define_h2o_X(),
undocumented_learner
data(mtcars)
# create task for prediction
mtcars_task <- sl3_Task$new(
data = mtcars,
covariates = c(
"cyl", "disp", "hp", "drat", "wt", "qsec", "vs", "am",
"gear", "carb"
),
outcome = "mpg"
)
# initialization, training, and prediction with the defaults
svm_lrnr <- Lrnr_svm$new()
svm_fit <- svm_lrnr$train(mtcars_task)
svm_preds <- svm_fit$predict()